Earthquakes are among the most immediate and deadly natural disasters that humans face. Accurately forecasting the extent of earthquake damage and assessing potential risks can be instrumental in saving numerous lives. In this study, we developed linear regression models capable of predicting seismic intensity distributions based on earthquake parameters: location, depth, and magnitude. Because it is completely data-driven, it can predict intensity distributions without geographical information. The dataset comprises seismic intensity data from earthquakes that occurred in the vicinity of Japan between 1997 and 2020, specifically containing 1,857 instances of earthquakes with a magnitude of 5.0 or greater, sourced from the Japan Meteorological Agency. We trained both regression and classification models and combined them to take advantage of both to create a hybrid model. The proposed model outperformed commonly used Ground Motion Prediction Equations (GMPEs) in terms of the correlation coefficient, F1 score, and MCC. Furthermore, the proposed model can predict even abnormal seismic intensity distributions, a task at conventional GMPEs often struggle.

通过开发基于地震参数（位置、深度和震级）的线性回归模型，本研究旨在准确预测地震强度分布，而无需地理信息。该模型基于1997年至2020年间在日本附近发生的大于5.0级的1,857次地震的地震强度数据，通过训练回归和分类模型以及结合二者，提出了一种混合模型，相对于常用的地面运动预测方程（GMPEs），该模型在相关系数、F1分数和MCC方面表现更好，甚至可以预测异常地震强度分布。

基于数据驱动的地震强度分布预测及混合分类-回归模型研究